class_docstr: Hugging Face Transformers PostEncoder.
code: def convert_examples_to_features(self, data, max_seq_length):
      input_id_features = []
      input_mask_features = []
      segment_ids_feature = []
      position_ids_feature = []
      input_id_length = []
      for text_id in range(len(data)):
          tokens_a = self.tokenizer.tokenize(data[text_id])
          if len(tokens_a) > max_seq_length - 2:
              tokens_a = tokens_a[: (max_seq_length - 2)]
          tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
          segment_ids = [0] * len(tokens)
  
          input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
          input_mask = [1] * len(input_ids)
          input_id_length.append(len(input_ids))
          # Zero-pad up to the sequence length.
          padding = [0] * (max_seq_length - len(input_ids))
          input_ids += padding
          input_mask += padding
          segment_ids += padding
          position_ids = [i for i in range(max_seq_length)]
  
          assert len(input_ids) == max_seq_length
          assert len(input_mask) == max_seq_length
          assert len(segment_ids) == max_seq_length
          assert len(position_ids) == max_seq_length
          input_id_features.append(input_ids)
          input_mask_features.append(input_mask)
          segment_ids_feature.append(segment_ids)
          position_ids_feature.append(position_ids)
      return (
          input_id_features,
          input_mask_features,
          segment_ids_feature,
          position_ids_feature,
          input_id_length,
      )
  
file_path: espnet/espnet2/slu/postdecoder/hugging_face_transformers_postdecoder.py
imports: from espnet2.slu.postdecoder.abs_postdecoder import AbsPostDecoder
  import logging
  import torch
  from typeguard import check_argument_types
label_desc: Use image node_id 4 for calling the HuggingFaceTransformersPostDecoder obj's underlying member method code with example usage: obj.convert_examples_to_features(data, max_seq_length) and returns: input_id_features,  input_mask_features,  segment_ids_feature,  position_ids_feature,  input_id_length